
def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     list[list[float]])   ->       list[list[float]]:






							'''simple docstring'''





							__UpperCamelCase      :  list[list[float]]       =   []
							for data in source_data:
														for i, el in enumerate(_lowerCamelCase):
																					if len(_lowerCamelCase) < i + 1:
																												data_lists.append([])
																					data_lists[i].append(float(_lowerCamelCase))
							return data_lists




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     list[list[float]]		,    _lowerCamelCase					:     list[int])   ->       list[list[float]]:






							'''simple docstring'''





							__UpperCamelCase      :  list[list[float]]       =   []
							for dlist, weight in zip(_lowerCamelCase		,    _lowerCamelCase):
														__UpperCamelCase      :  Tuple       =   min(_lowerCamelCase)
														__UpperCamelCase      :  Optional[Any]       =   max(_lowerCamelCase)

														__UpperCamelCase      :  list[float]       =   []
														# for weight 0 score is 1 - actual score
														if weight == 0:
																					for item in dlist:
																												try:
																																			score.append(1 - ((item - mind) / (maxd - mind)))
																												except ZeroDivisionError:
																																			score.append(1)

														elif weight == 1:
																					for item in dlist:
																												try:
																																			score.append((item - mind) / (maxd - mind))
																												except ZeroDivisionError:
																																			score.append(0)

        # weight not 0 or 1
														else:
																					__UpperCamelCase      :  Any       =   F'Invalid weight of {weight:f} provided'
																					raise ValueError(_lowerCamelCase)

														score_lists.append(_lowerCamelCase)

							return score_lists




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     list[list[float]])   ->       list[float]:






							'''simple docstring'''





							__UpperCamelCase      :  list[float]       =   [0 for i in range(len(score_lists[0]))]

							for slist in score_lists:
														for j, ele in enumerate(_lowerCamelCase):
																					__UpperCamelCase      :  Optional[Any]       =   final_scores[j] + ele

							return final_scores




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     list[list[float]]		,    _lowerCamelCase					:     list[int])   ->       list[list[float]]:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   get_data(_lowerCamelCase)
							__UpperCamelCase      :  Any       =   calculate_each_score(_lowerCamelCase		,    _lowerCamelCase)
							__UpperCamelCase      :  Union[str, Any]       =   generate_final_scores(_lowerCamelCase)

							# append scores to source data
							for i, ele in enumerate(_lowerCamelCase):
														source_data[i].append(_lowerCamelCase)

							return source_data

import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


lowercase    :   List[str]						=			logging.get_logger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  *a						:Tuple	,  **a						:List[Any]		)						->					None:
								warnings.warn(
								    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
								    " Please use PerceiverImageProcessor instead."	,  a	,  )
								super().__init__(*a	,  **a		)